ANCOVA in
Reading Words (1 Min) (Reading Words (1 Min))
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Reading Words (1 Min) (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Reading Words (1 Min) (measured using pre- and post-tests).
Setting Initial Variables
dv = "TFL.lidas.per.min"
dv.pos = "TFL.lidas.per.min.pos"
dv.pre = "TFL.lidas.per.min.pre"
fatores2 <- c("Sexo","Zona","Cor.Raca","Serie","TFL.lidas.per.min.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
"Parda"="#b97100","Indígena"="#9F262F",
"Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:Sexo"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
"Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
"Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
"Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
"Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
"Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
"Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
"Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
"Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
"Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
"Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)
for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
"TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
"leitura.compreensao")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "sumary")
gdat <- gdat[which(is.na(gdat$Necessidade.Deficiencia) & !is.na(gdat$Stari.Grupo)),]
dat <- gdat
dat$grupo <- factor(dat[["Stari.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
## There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pre |
91 |
91.923 |
94.0 |
27 |
154 |
24.115 |
2.528 |
5.022 |
30.50 |
YES |
-0.184 |
-0.128 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pre |
53 |
93.528 |
95.0 |
54 |
135 |
19.277 |
2.648 |
5.313 |
27.00 |
YES |
-0.069 |
-0.471 |
|
|
|
|
|
|
TFL.lidas.per.min.pre |
144 |
92.514 |
95.0 |
27 |
154 |
22.399 |
1.867 |
3.690 |
29.00 |
YES |
-0.183 |
-0.033 |
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pos |
91 |
113.593 |
114.0 |
43 |
187 |
28.508 |
2.988 |
5.937 |
44.00 |
YES |
0.253 |
-0.327 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pos |
53 |
114.774 |
112.0 |
67 |
195 |
27.425 |
3.767 |
7.559 |
40.00 |
NO |
0.701 |
0.273 |
|
|
|
|
|
|
TFL.lidas.per.min.pos |
144 |
114.028 |
112.0 |
43 |
195 |
28.024 |
2.335 |
4.616 |
42.00 |
YES |
0.408 |
-0.068 |
| Controle |
F |
|
|
|
|
TFL.lidas.per.min.pre |
36 |
93.917 |
96.5 |
40 |
154 |
24.110 |
4.018 |
8.158 |
23.75 |
YES |
-0.129 |
0.165 |
| Controle |
M |
|
|
|
|
TFL.lidas.per.min.pre |
55 |
90.618 |
92.0 |
27 |
141 |
24.250 |
3.270 |
6.556 |
33.50 |
YES |
-0.214 |
-0.440 |
| Experimental |
F |
|
|
|
|
TFL.lidas.per.min.pre |
22 |
98.455 |
96.5 |
60 |
135 |
17.470 |
3.725 |
7.746 |
22.25 |
YES |
-0.190 |
-0.260 |
| Experimental |
M |
|
|
|
|
TFL.lidas.per.min.pre |
31 |
90.032 |
90.0 |
54 |
135 |
20.001 |
3.592 |
7.336 |
19.50 |
YES |
0.105 |
-0.607 |
| Controle |
F |
|
|
|
|
TFL.lidas.per.min.pos |
36 |
121.194 |
118.5 |
87 |
187 |
26.148 |
4.358 |
8.847 |
37.00 |
NO |
0.615 |
-0.443 |
| Controle |
M |
|
|
|
|
TFL.lidas.per.min.pos |
55 |
108.618 |
108.0 |
43 |
176 |
29.114 |
3.926 |
7.871 |
42.00 |
YES |
0.196 |
-0.580 |
| Experimental |
F |
|
|
|
|
TFL.lidas.per.min.pos |
22 |
125.318 |
117.5 |
82 |
195 |
26.032 |
5.550 |
11.542 |
38.75 |
NO |
0.587 |
0.116 |
| Experimental |
M |
|
|
|
|
TFL.lidas.per.min.pos |
31 |
107.290 |
101.0 |
67 |
184 |
26.267 |
4.718 |
9.635 |
26.00 |
NO |
0.962 |
0.822 |
| Controle |
|
Rural |
|
|
|
TFL.lidas.per.min.pre |
58 |
91.914 |
94.5 |
27 |
154 |
26.233 |
3.445 |
6.898 |
38.25 |
YES |
-0.189 |
-0.347 |
| Controle |
|
Urbana |
|
|
|
TFL.lidas.per.min.pre |
7 |
93.000 |
99.0 |
48 |
124 |
24.055 |
9.092 |
22.248 |
19.00 |
NO |
-0.586 |
-0.815 |
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pre |
26 |
91.654 |
92.0 |
54 |
139 |
19.626 |
3.849 |
7.927 |
22.25 |
YES |
0.088 |
-0.247 |
| Experimental |
|
Rural |
|
|
|
TFL.lidas.per.min.pre |
35 |
90.714 |
90.0 |
54 |
135 |
18.909 |
3.196 |
6.496 |
19.00 |
YES |
0.062 |
-0.526 |
| Experimental |
|
Urbana |
|
|
|
TFL.lidas.per.min.pre |
6 |
102.667 |
95.5 |
90 |
135 |
17.455 |
7.126 |
18.318 |
15.25 |
NO |
0.921 |
-0.930 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pre |
12 |
97.167 |
98.5 |
54 |
117 |
20.683 |
5.971 |
13.141 |
32.00 |
NO |
-0.611 |
-0.922 |
| Controle |
|
Rural |
|
|
|
TFL.lidas.per.min.pos |
58 |
106.276 |
105.5 |
43 |
187 |
25.144 |
3.302 |
6.611 |
31.50 |
YES |
0.330 |
0.649 |
| Controle |
|
Urbana |
|
|
|
TFL.lidas.per.min.pos |
7 |
142.857 |
150.0 |
88 |
176 |
28.399 |
10.734 |
26.264 |
23.00 |
NO |
-0.735 |
-0.690 |
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pos |
26 |
122.038 |
128.0 |
68 |
176 |
29.198 |
5.726 |
11.793 |
42.25 |
YES |
0.007 |
-1.036 |
| Experimental |
|
Rural |
|
|
|
TFL.lidas.per.min.pos |
35 |
109.371 |
105.0 |
70 |
184 |
23.204 |
3.922 |
7.971 |
22.50 |
NO |
1.008 |
1.248 |
| Experimental |
|
Urbana |
|
|
|
TFL.lidas.per.min.pos |
6 |
134.333 |
123.0 |
99 |
195 |
34.541 |
14.101 |
36.248 |
31.00 |
NO |
0.685 |
-1.190 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pos |
12 |
120.750 |
133.0 |
67 |
166 |
31.758 |
9.168 |
20.178 |
48.75 |
YES |
-0.359 |
-1.448 |
| Controle |
|
|
Parda |
|
|
TFL.lidas.per.min.pre |
42 |
92.381 |
95.0 |
27 |
135 |
23.486 |
3.624 |
7.319 |
33.00 |
NO |
-0.511 |
-0.178 |
| Controle |
|
|
Indígena |
|
|
TFL.lidas.per.min.pre |
3 |
117.000 |
101.0 |
96 |
154 |
32.140 |
18.556 |
79.841 |
29.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Branca |
|
|
TFL.lidas.per.min.pre |
12 |
105.500 |
103.5 |
90 |
141 |
15.895 |
4.588 |
10.099 |
25.00 |
NO |
0.771 |
-0.536 |
| Controle |
|
|
Preta |
|
|
TFL.lidas.per.min.pre |
1 |
99.000 |
99.0 |
99 |
99 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pre |
33 |
83.909 |
85.0 |
40 |
139 |
24.108 |
4.197 |
8.548 |
39.00 |
YES |
0.119 |
-0.757 |
| Experimental |
|
|
Parda |
|
|
TFL.lidas.per.min.pre |
16 |
88.688 |
86.0 |
54 |
135 |
20.261 |
5.065 |
10.796 |
17.00 |
NO |
0.549 |
-0.209 |
| Experimental |
|
|
Indígena |
|
|
TFL.lidas.per.min.pre |
8 |
95.125 |
98.5 |
54 |
115 |
19.342 |
6.839 |
16.171 |
14.25 |
NO |
-1.028 |
-0.229 |
| Experimental |
|
|
Branca |
|
|
TFL.lidas.per.min.pre |
5 |
104.000 |
96.0 |
95 |
117 |
11.874 |
5.310 |
14.744 |
22.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
Amarela |
|
|
TFL.lidas.per.min.pre |
1 |
76.000 |
76.0 |
76 |
76 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pre |
23 |
94.826 |
95.0 |
59 |
135 |
19.878 |
4.145 |
8.596 |
24.00 |
YES |
-0.085 |
-0.758 |
| Controle |
|
|
Parda |
|
|
TFL.lidas.per.min.pos |
42 |
101.095 |
96.0 |
43 |
187 |
25.697 |
3.965 |
8.008 |
29.00 |
NO |
0.745 |
1.536 |
| Controle |
|
|
Indígena |
|
|
TFL.lidas.per.min.pos |
3 |
118.667 |
117.0 |
92 |
147 |
27.538 |
15.899 |
68.408 |
27.50 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Branca |
|
|
TFL.lidas.per.min.pos |
12 |
115.417 |
116.5 |
87 |
150 |
18.143 |
5.237 |
11.528 |
15.75 |
YES |
0.092 |
-0.875 |
| Controle |
|
|
Preta |
|
|
TFL.lidas.per.min.pos |
1 |
118.000 |
118.0 |
118 |
118 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pos |
33 |
128.242 |
135.0 |
68 |
176 |
29.130 |
5.071 |
10.329 |
42.00 |
YES |
-0.309 |
-0.824 |
| Experimental |
|
|
Parda |
|
|
TFL.lidas.per.min.pos |
16 |
100.750 |
97.0 |
70 |
145 |
19.039 |
4.760 |
10.145 |
10.75 |
NO |
0.737 |
0.167 |
| Experimental |
|
|
Indígena |
|
|
TFL.lidas.per.min.pos |
8 |
106.875 |
112.0 |
67 |
139 |
21.290 |
7.527 |
17.799 |
16.75 |
YES |
-0.427 |
-0.751 |
| Experimental |
|
|
Branca |
|
|
TFL.lidas.per.min.pos |
5 |
126.600 |
121.0 |
110 |
151 |
18.609 |
8.322 |
23.106 |
31.00 |
YES |
0.254 |
-2.082 |
| Experimental |
|
|
Amarela |
|
|
TFL.lidas.per.min.pos |
1 |
82.000 |
82.0 |
82 |
82 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pos |
23 |
126.130 |
130.0 |
80 |
195 |
30.697 |
6.401 |
13.274 |
43.50 |
YES |
0.450 |
-0.569 |
| Controle |
|
|
|
6 ano |
|
TFL.lidas.per.min.pre |
31 |
89.355 |
93.0 |
27 |
141 |
25.605 |
4.599 |
9.392 |
22.00 |
YES |
-0.403 |
0.118 |
| Controle |
|
|
|
7 ano |
|
TFL.lidas.per.min.pre |
19 |
96.211 |
99.0 |
54 |
154 |
23.797 |
5.459 |
11.470 |
29.50 |
YES |
0.281 |
-0.122 |
| Controle |
|
|
|
8 ano |
|
TFL.lidas.per.min.pre |
15 |
88.667 |
86.0 |
51 |
135 |
25.519 |
6.589 |
14.132 |
29.50 |
YES |
0.143 |
-1.110 |
| Controle |
|
|
|
9 ano |
|
TFL.lidas.per.min.pre |
26 |
93.731 |
97.0 |
54 |
130 |
22.413 |
4.396 |
9.053 |
36.50 |
YES |
-0.316 |
-1.130 |
| Experimental |
|
|
|
6 ano |
|
TFL.lidas.per.min.pre |
19 |
89.368 |
90.0 |
59 |
117 |
17.824 |
4.089 |
8.591 |
19.50 |
YES |
-0.121 |
-1.106 |
| Experimental |
|
|
|
7 ano |
|
TFL.lidas.per.min.pre |
11 |
93.636 |
90.0 |
66 |
117 |
17.580 |
5.301 |
11.810 |
26.50 |
YES |
0.127 |
-1.451 |
| Experimental |
|
|
|
8 ano |
|
TFL.lidas.per.min.pre |
13 |
93.462 |
95.0 |
54 |
135 |
23.642 |
6.557 |
14.287 |
29.00 |
YES |
-0.128 |
-0.872 |
| Experimental |
|
|
|
9 ano |
|
TFL.lidas.per.min.pre |
10 |
101.400 |
100.5 |
68 |
135 |
17.821 |
5.636 |
12.749 |
14.25 |
YES |
0.035 |
-0.403 |
| Controle |
|
|
|
6 ano |
|
TFL.lidas.per.min.pos |
31 |
103.000 |
96.0 |
43 |
158 |
25.616 |
4.601 |
9.396 |
29.50 |
YES |
0.077 |
-0.305 |
| Controle |
|
|
|
7 ano |
|
TFL.lidas.per.min.pos |
19 |
113.684 |
102.0 |
87 |
187 |
26.719 |
6.130 |
12.878 |
35.50 |
NO |
1.074 |
0.543 |
| Controle |
|
|
|
8 ano |
|
TFL.lidas.per.min.pos |
15 |
123.533 |
135.0 |
70 |
176 |
32.089 |
8.285 |
17.770 |
43.00 |
YES |
-0.126 |
-1.208 |
| Controle |
|
|
|
9 ano |
|
TFL.lidas.per.min.pos |
26 |
120.423 |
117.0 |
67 |
176 |
28.297 |
5.550 |
11.430 |
34.50 |
YES |
-0.064 |
-0.663 |
| Experimental |
|
|
|
6 ano |
|
TFL.lidas.per.min.pos |
19 |
116.842 |
105.0 |
70 |
195 |
34.656 |
7.951 |
16.704 |
47.00 |
NO |
0.812 |
-0.436 |
| Experimental |
|
|
|
7 ano |
|
TFL.lidas.per.min.pos |
11 |
107.727 |
102.0 |
82 |
141 |
18.714 |
5.643 |
12.572 |
26.50 |
YES |
0.325 |
-1.368 |
| Experimental |
|
|
|
8 ano |
|
TFL.lidas.per.min.pos |
13 |
113.000 |
115.0 |
67 |
166 |
28.589 |
7.929 |
17.276 |
36.00 |
YES |
0.074 |
-1.092 |
| Experimental |
|
|
|
9 ano |
|
TFL.lidas.per.min.pos |
10 |
120.900 |
113.5 |
95 |
151 |
18.675 |
5.906 |
13.360 |
27.50 |
YES |
0.211 |
-1.594 |
| Controle |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pre |
15 |
53.267 |
54.0 |
27 |
67 |
9.669 |
2.497 |
5.355 |
5.50 |
NO |
-1.196 |
1.265 |
| Controle |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pre |
19 |
78.263 |
81.0 |
68 |
86 |
6.814 |
1.563 |
3.284 |
11.00 |
YES |
-0.392 |
-1.481 |
| Controle |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pre |
15 |
91.600 |
92.0 |
88 |
95 |
2.530 |
0.653 |
1.401 |
3.50 |
YES |
-0.072 |
-1.435 |
| Controle |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pre |
23 |
102.609 |
102.0 |
96 |
110 |
4.261 |
0.889 |
1.843 |
6.50 |
YES |
0.475 |
-1.090 |
| Controle |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pre |
19 |
123.421 |
118.0 |
112 |
154 |
11.217 |
2.573 |
5.406 |
10.00 |
NO |
1.297 |
0.630 |
| Experimental |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pre |
5 |
58.600 |
59.0 |
54 |
66 |
4.980 |
2.227 |
6.183 |
6.00 |
YES |
0.346 |
-1.732 |
| Experimental |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pre |
12 |
77.417 |
79.5 |
68 |
86 |
6.360 |
1.836 |
4.041 |
8.00 |
YES |
-0.441 |
-1.376 |
| Experimental |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pre |
13 |
91.923 |
90.0 |
88 |
95 |
2.900 |
0.804 |
1.752 |
5.00 |
YES |
-0.005 |
-1.903 |
| Experimental |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pre |
11 |
100.818 |
99.0 |
96 |
110 |
5.382 |
1.623 |
3.616 |
8.00 |
NO |
0.661 |
-1.378 |
| Experimental |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pre |
12 |
119.250 |
117.0 |
113 |
135 |
7.485 |
2.161 |
4.756 |
0.50 |
NO |
1.445 |
0.342 |
| Controle |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pos |
15 |
93.467 |
88.0 |
43 |
149 |
29.715 |
7.672 |
16.456 |
33.50 |
YES |
0.326 |
-0.949 |
| Controle |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pos |
19 |
104.632 |
95.0 |
67 |
139 |
24.683 |
5.663 |
11.897 |
44.50 |
YES |
0.214 |
-1.577 |
| Controle |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pos |
15 |
107.067 |
96.0 |
81 |
150 |
22.827 |
5.894 |
12.641 |
36.50 |
NO |
0.552 |
-1.311 |
| Controle |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pos |
23 |
124.174 |
118.0 |
91 |
171 |
23.704 |
4.943 |
10.250 |
32.00 |
YES |
0.448 |
-0.945 |
| Controle |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pos |
19 |
130.789 |
128.0 |
90 |
187 |
27.614 |
6.335 |
13.309 |
25.00 |
NO |
0.520 |
-0.698 |
| Experimental |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pos |
5 |
96.000 |
93.0 |
67 |
150 |
32.435 |
14.505 |
40.273 |
20.00 |
NO |
0.726 |
-1.300 |
| Experimental |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pos |
12 |
93.500 |
92.5 |
70 |
145 |
18.594 |
5.368 |
11.814 |
14.75 |
NO |
1.535 |
2.169 |
| Experimental |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pos |
13 |
111.231 |
110.0 |
93 |
139 |
14.066 |
3.901 |
8.500 |
16.00 |
NO |
0.566 |
-0.740 |
| Experimental |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pos |
11 |
125.000 |
115.0 |
95 |
195 |
28.415 |
8.567 |
19.089 |
25.50 |
NO |
1.235 |
0.597 |
| Experimental |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pos |
12 |
138.333 |
137.0 |
99 |
184 |
22.329 |
6.446 |
14.187 |
18.00 |
YES |
0.316 |
-0.433 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "TFL.lidas.per.min.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["TFL.lidas.per.min"]] <- c(pdat[["TFL.lidas.per.min.pre"]], pdat[["TFL.lidas.per.min.pos"]])
aov = anova_test(pdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, TFL.lidas.per.min.pos ~ grupo, covariate = TFL.lidas.per.min.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "TFL.lidas.per.min.pos", "grupo", covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["TFL.lidas.per.min"]] <- c(wdat[["TFL.lidas.per.min.pre"]], wdat[["TFL.lidas.per.min.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## [1] "P463" "P956" "P3498" "P3609" "P3721" "P3516" "P808" "P445" "P3521" "P3504" "P1010" "P3729" "P446" "P1109" "P465"
## [16] "P3492" "P942" "P3581" "P1091" "P820" "P1094" "P3627" "P621" "P3566" "P184" "P133" "P628" "P3501" "P816" "P1017"
## [31] "P950" "P947" "P1097" "P3495" "P3615" "P1716" "P3505" "P3512" "P3524" "P3565" "P3502" "P128" "P1089" "P812" "P521"
## [46] "P448" "P3558" "P3606" "P3518" "P3624" "P1004" "P994" "P1000" "P616" "P618" "P829" "P241" "P451" "P515" "P615"
## [61] "P460" "P813" "P936" "P614" "P457" "P513" "P958" "P516" "P1084" "P997" "P626" "P3509" "P3734" "P613" "P3598"
## [76] "P1107" "P512" "P464" "P625" "P612" "P3560" "P3519" "P620" "P523" "P948" "P2210" "P3508" "P943" "P3577" "P176"
## [91] "P3569" "P1742"
aov = anova_test(wdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 TFL.lidas.per.min.pre 1 49 39.759 7.9e-08 * 0.448
## 2 grupo 1 49 0.929 3.4e-01 0.019
| TFL.lidas.per.min.pre |
1 |
49 |
39.759 |
0.00 |
* |
0.448 |
| grupo |
1 |
49 |
0.929 |
0.34 |
|
0.019 |
pwc <- emmeans_test(wdat, TFL.lidas.per.min.pos ~ grupo, covariate = TFL.lidas.per.min.pre,
p.adjust.method = "bonferroni")
| TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
49 |
0.964 |
0.34 |
0.34 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
TFL.lidas.per.min |
pre |
pos |
100 |
-9.206 |
0 |
0 |
**** |
| Experimental |
time |
TFL.lidas.per.min |
pre |
pos |
100 |
-5.917 |
0 |
0 |
**** |
ds <- get.descriptives(wdat, "TFL.lidas.per.min.pos", "grupo", covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
33 |
87.364 |
4.315 |
139.000 |
3.971 |
141.414 |
3.045 |
135.295 |
147.532 |
| Experimental |
19 |
96.947 |
4.019 |
140.684 |
5.427 |
136.492 |
4.036 |
128.382 |
144.602 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "TFL.lidas.per.min.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "TFL.lidas.per.min.pos", "grupo", aov, pwc, covar = "TFL.lidas.per.min.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Words (1 Min)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "TFL.lidas.per.min", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Words (1 Min)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.950 0.0305
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 50 1.37 0.248
ANCOVA and
Pairwise for two factors grupo:Sexo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
"TFL.lidas.per.min.pos", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
pdat[["Sexo"]],
level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["TFL.lidas.per.min"]] <- c(pdat[["TFL.lidas.per.min.pre"]], pdat[["TFL.lidas.per.min.pos"]])
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(pdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(pdat, grupo), TFL.lidas.per.min.pos ~ Sexo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Sexo), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(pdat, "TFL.lidas.per.min.pos", c("grupo","Sexo"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Sexo"]])) >= 2) {
wdat = pdat
res = residuals(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Sexo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["TFL.lidas.per.min"]] <- c(wdat[["TFL.lidas.per.min.pre"]], wdat[["TFL.lidas.per.min.pos"]])
ldat[["grupo:Sexo"]] = wdat
(non.normal)
}
## [1] "P1768" "P121" "P2250" "P2380" "P1743" "P2220" "P2378" "P510" "P2385" "P2404" "P2214" "P2377" "P2294" "P1698" "P945"
## [16] "P2209"
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(wdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| TFL.lidas.per.min.pre |
1 |
123 |
81.449 |
0.000 |
* |
0.398 |
| grupo |
1 |
123 |
0.506 |
0.478 |
|
0.004 |
| Sexo |
1 |
123 |
11.520 |
0.001 |
* |
0.086 |
| grupo:Sexo |
1 |
123 |
0.986 |
0.323 |
|
0.008 |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(wdat, grupo), TFL.lidas.per.min.pos ~ Sexo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Sexo), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
|
F |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
123 |
0.332 |
0.741 |
0.741 |
ns |
|
M |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
123 |
-1.177 |
0.241 |
0.241 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*Sexo |
TFL.lidas.per.min.pos |
F |
M |
123 |
3.299 |
0.001 |
0.001 |
** |
| Experimental |
|
TFL.lidas.per.min.pre*Sexo |
TFL.lidas.per.min.pos |
F |
M |
123 |
1.293 |
0.198 |
0.198 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Sexo"]],
by=c("grupo","Sexo","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
TFL.lidas.per.min |
pre |
pos |
248 |
-4.063 |
0.000 |
0.000 |
**** |
| Controle |
M |
time |
TFL.lidas.per.min |
pre |
pos |
248 |
-2.189 |
0.030 |
0.030 |
* |
| Experimental |
F |
time |
TFL.lidas.per.min |
pre |
pos |
248 |
-2.483 |
0.014 |
0.014 |
* |
| Experimental |
M |
time |
TFL.lidas.per.min |
pre |
pos |
248 |
-2.710 |
0.007 |
0.007 |
** |
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(wdat, "TFL.lidas.per.min.pos", c("grupo","Sexo"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
by=c("grupo","Sexo"), suffixes = c("","'"))
}
| Controle |
F |
32 |
93.125 |
4.171 |
115.625 |
3.688 |
115.384 |
3.080 |
109.287 |
121.480 |
| Controle |
M |
47 |
91.213 |
3.550 |
101.213 |
3.485 |
102.203 |
2.544 |
97.168 |
107.238 |
| Experimental |
F |
19 |
101.474 |
3.288 |
119.316 |
4.743 |
113.698 |
4.045 |
105.691 |
121.705 |
| Experimental |
M |
30 |
89.233 |
3.621 |
104.733 |
4.099 |
106.998 |
3.191 |
100.682 |
113.314 |
Plots for ancova
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "TFL.lidas.per.min.pos", c("grupo","Sexo"), aov, pwcs, covar = "TFL.lidas.per.min.pre",
theme = "classic", color = color[["grupo:Sexo"]],
subtitle = which(aov$Effect == "grupo:Sexo"))
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots[["grupo:Sexo"]] + ggplot2::ylab("Reading Words (1 Min)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "TFL.lidas.per.min", c("grupo","Sexo"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Sexo"]])) >= 2)
plots[["grupo:Sexo"]] + ggplot2::ylab("Reading Words (1 Min)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
facet.by = c("grupo","Sexo"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "grupo", facet.by = "Sexo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "Sexo", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Sexo"]])) >= 2)
res <- augment(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Sexo, data = wdat))
if (length(unique(pdat[["Sexo"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.972 0.0102
if (length(unique(pdat[["Sexo"]])) >= 2)
levene_test(res, .resid ~ grupo*Sexo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 124 1.04 0.376
ANCOVA and
Pairwise for two factors grupo:Zona
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
"TFL.lidas.per.min.pos", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
pdat[["Zona"]],
level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["TFL.lidas.per.min"]] <- c(pdat[["TFL.lidas.per.min.pre"]], pdat[["TFL.lidas.per.min.pos"]])
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(pdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Zona)
laov[["grupo:Zona"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(pdat, grupo), TFL.lidas.per.min.pos ~ Zona,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Zona), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(pdat, "TFL.lidas.per.min.pos", c("grupo","Zona"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Zona"]])) >= 2) {
wdat = pdat
res = residuals(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Zona, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["TFL.lidas.per.min"]] <- c(wdat[["TFL.lidas.per.min.pre"]], wdat[["TFL.lidas.per.min.pos"]])
ldat[["grupo:Zona"]] = wdat
(non.normal)
}
## [1] "P1768" "P121" "P2378" "P1743" "P2220" "P510" "P1698"
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(wdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Zona)
laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| TFL.lidas.per.min.pre |
1 |
94 |
84.400 |
0.000 |
* |
0.473 |
| grupo |
1 |
94 |
0.260 |
0.611 |
|
0.003 |
| Zona |
1 |
94 |
34.869 |
0.000 |
* |
0.271 |
| grupo:Zona |
1 |
94 |
8.262 |
0.005 |
* |
0.081 |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(wdat, grupo), TFL.lidas.per.min.pos ~ Zona,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Zona), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
|
Rural |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
94 |
-0.545 |
0.587 |
0.587 |
ns |
|
Urbana |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
94 |
2.869 |
0.005 |
0.005 |
** |
| Controle |
|
TFL.lidas.per.min.pre*Zona |
TFL.lidas.per.min.pos |
Rural |
Urbana |
94 |
-6.379 |
0.000 |
0.000 |
**** |
| Experimental |
|
TFL.lidas.per.min.pre*Zona |
TFL.lidas.per.min.pos |
Rural |
Urbana |
94 |
-1.565 |
0.121 |
0.121 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Zona"]],
by=c("grupo","Zona","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
TFL.lidas.per.min |
pre |
pos |
190 |
-2.549 |
0.012 |
0.012 |
* |
| Controle |
Urbana |
time |
TFL.lidas.per.min |
pre |
pos |
190 |
-4.227 |
0.000 |
0.000 |
**** |
| Experimental |
Rural |
time |
TFL.lidas.per.min |
pre |
pos |
190 |
-2.356 |
0.019 |
0.019 |
* |
| Experimental |
Urbana |
time |
TFL.lidas.per.min |
pre |
pos |
190 |
-1.505 |
0.134 |
0.134 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(wdat, "TFL.lidas.per.min.pos", c("grupo","Zona"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
by=c("grupo","Zona"), suffixes = c("","'"))
}
| Controle |
Rural |
55 |
92.764 |
3.462 |
103.491 |
2.981 |
103.544 |
2.066 |
99.442 |
107.646 |
| Controle |
Urbana |
7 |
93.000 |
9.092 |
142.857 |
10.734 |
142.762 |
5.791 |
131.264 |
154.260 |
| Experimental |
Rural |
32 |
91.656 |
3.195 |
104.656 |
3.030 |
105.402 |
2.710 |
100.022 |
110.782 |
| Experimental |
Urbana |
5 |
101.200 |
8.540 |
122.200 |
8.800 |
116.977 |
6.875 |
103.325 |
130.628 |
Plots for ancova
if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "TFL.lidas.per.min.pos", c("grupo","Zona"), aov, pwcs, covar = "TFL.lidas.per.min.pre",
theme = "classic", color = color[["grupo:Zona"]],
subtitle = which(aov$Effect == "grupo:Zona"))
}
if (length(unique(pdat[["Zona"]])) >= 2) {
plots[["grupo:Zona"]] + ggplot2::ylab("Reading Words (1 Min)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "TFL.lidas.per.min", c("grupo","Zona"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Zona"]])) >= 2)
plots[["grupo:Zona"]] + ggplot2::ylab("Reading Words (1 Min)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
facet.by = c("grupo","Zona"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "grupo", facet.by = "Zona", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "Zona", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Zona"]])) >= 2)
res <- augment(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Zona, data = wdat))
if (length(unique(pdat[["Zona"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.982 0.212
if (length(unique(pdat[["Zona"]])) >= 2)
levene_test(res, .resid ~ grupo*Zona)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 95 2.06 0.111
ANCOVA and
Pairwise for two factors grupo:Cor.Raca
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
"TFL.lidas.per.min.pos", c("grupo","Cor.Raca"))
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
pdat[["Cor.Raca"]],
level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["TFL.lidas.per.min"]] <- c(pdat[["TFL.lidas.per.min.pre"]], pdat[["TFL.lidas.per.min.pos"]])
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(pdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(pdat, grupo), TFL.lidas.per.min.pos ~ Cor.Raca,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Cor.Raca), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(pdat, "TFL.lidas.per.min.pos", c("grupo","Cor.Raca"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
wdat = pdat
res = residuals(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Cor.Raca, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["TFL.lidas.per.min"]] <- c(wdat[["TFL.lidas.per.min.pre"]], wdat[["TFL.lidas.per.min.pos"]])
ldat[["grupo:Cor.Raca"]] = wdat
(non.normal)
}
## [1] "P510" "P945"
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(wdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| TFL.lidas.per.min.pre |
1 |
68 |
54.835 |
0.000 |
* |
0.446 |
| grupo |
1 |
68 |
2.749 |
0.102 |
|
0.039 |
| Cor.Raca |
1 |
68 |
2.633 |
0.109 |
|
0.037 |
| grupo:Cor.Raca |
1 |
68 |
1.775 |
0.187 |
|
0.025 |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(wdat, grupo), TFL.lidas.per.min.pos ~ Cor.Raca,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Cor.Raca), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
|
Parda |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
68 |
-0.818 |
0.416 |
0.416 |
ns |
|
Branca |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
68 |
-1.965 |
0.054 |
0.054 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*Cor.Raca |
TFL.lidas.per.min.pos |
Parda |
Branca |
68 |
-0.658 |
0.513 |
0.513 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*Cor.Raca |
TFL.lidas.per.min.pos |
Parda |
Branca |
68 |
-2.019 |
0.047 |
0.047 |
* |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Parda |
time |
TFL.lidas.per.min |
pre |
pos |
138 |
-1.587 |
0.115 |
0.115 |
ns |
| Controle |
Branca |
time |
TFL.lidas.per.min |
pre |
pos |
138 |
-0.605 |
0.546 |
0.546 |
ns |
| Experimental |
Parda |
time |
TFL.lidas.per.min |
pre |
pos |
138 |
-1.640 |
0.103 |
0.103 |
ns |
| Experimental |
Branca |
time |
TFL.lidas.per.min |
pre |
pos |
138 |
-1.718 |
0.088 |
0.088 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(wdat, "TFL.lidas.per.min.pos", c("grupo","Cor.Raca"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}
| Controle |
Branca |
11 |
106.909 |
4.783 |
112.273 |
4.589 |
104.105 |
4.735 |
94.656 |
113.554 |
| Controle |
Parda |
41 |
91.707 |
3.649 |
99.000 |
3.449 |
100.585 |
2.395 |
95.806 |
105.364 |
| Experimental |
Branca |
5 |
104.000 |
5.310 |
126.600 |
8.322 |
120.298 |
6.883 |
106.563 |
134.033 |
| Experimental |
Parda |
16 |
88.688 |
5.065 |
100.750 |
4.760 |
104.273 |
3.848 |
96.595 |
111.951 |
Plots for ancova
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "TFL.lidas.per.min.pos", c("grupo","Cor.Raca"), aov, pwcs, covar = "TFL.lidas.per.min.pre",
theme = "classic", color = color[["grupo:Cor.Raca"]],
subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Reading Words (1 Min)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "TFL.lidas.per.min", c("grupo","Cor.Raca"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Reading Words (1 Min)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
res <- augment(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Cor.Raca, data = wdat))
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.968 0.0636
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
levene_test(res, .resid ~ grupo*Cor.Raca)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 69 0.135 0.939
ANCOVA and
Pairwise for two factors grupo:Serie
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
"TFL.lidas.per.min.pos", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
pdat[["Serie"]],
level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["TFL.lidas.per.min"]] <- c(pdat[["TFL.lidas.per.min.pre"]], pdat[["TFL.lidas.per.min.pos"]])
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(pdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Serie)
laov[["grupo:Serie"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(pdat, grupo), TFL.lidas.per.min.pos ~ Serie,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Serie), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(pdat, "TFL.lidas.per.min.pos", c("grupo","Serie"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Serie"]])) >= 2) {
wdat = pdat
res = residuals(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Serie, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["TFL.lidas.per.min"]] <- c(wdat[["TFL.lidas.per.min.pre"]], wdat[["TFL.lidas.per.min.pos"]])
ldat[["grupo:Serie"]] = wdat
(non.normal)
}
## [1] "P3609" "P3498" "P1010" "P3721" "P956" "P808" "P3729" "P1109" "P1017" "P463" "P1091" "P820" "P1094" "P621" "P628"
## [16] "P133" "P994" "P1097" "P3516" "P1716" "P3505" "P942" "P3581" "P128" "P445" "P3627" "P3521" "P3504" "P3566" "P446"
## [31] "P997" "P616" "P1089" "P829" "P3615" "P947" "P465" "P3492" "P451" "P184" "P618" "P614" "P2210" "P3606" "P950"
## [46] "P3558" "P3624" "P3501" "P1004" "P3734" "P816" "P1000" "P3509" "P1107" "P3565" "P3495" "P615" "P625" "P612" "P241"
## [61] "P3512" "P3524" "P521" "P3502" "P620" "P1084" "P626" "P613" "P812" "P2239" "P448" "P936" "P3518" "P513" "P516"
## [76] "P3598" "P143" "P1742" "P515" "P459" "P3560" "P958" "P523" "P460" "P813" "P948" "P622" "P457" "P943" "P512"
## [91] "P238" "P3569" "P464" "P449" "P3519" "P2249" "P3577" "P121" "P176"
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(wdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Serie)
laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| TFL.lidas.per.min.pre |
1 |
36 |
78.106 |
0.000 |
* |
0.685 |
| grupo |
1 |
36 |
0.017 |
0.896 |
|
0.000 |
| Serie |
3 |
36 |
2.179 |
0.107 |
|
0.154 |
| grupo:Serie |
3 |
36 |
10.000 |
0.000 |
* |
0.455 |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(wdat, grupo), TFL.lidas.per.min.pos ~ Serie,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Serie), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
|
6 ano |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
-4.437 |
0.000 |
0.000 |
**** |
|
7 ano |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
1.780 |
0.083 |
0.083 |
ns |
|
8 ano |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
2.164 |
0.037 |
0.037 |
* |
|
9 ano |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
1.877 |
0.069 |
0.069 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
6 ano |
7 ano |
36 |
-1.107 |
0.275 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
6 ano |
8 ano |
36 |
-3.368 |
0.002 |
0.011 |
* |
| Controle |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
6 ano |
9 ano |
36 |
-3.410 |
0.002 |
0.010 |
** |
| Controle |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
7 ano |
8 ano |
36 |
-1.843 |
0.074 |
0.441 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
7 ano |
9 ano |
36 |
-1.697 |
0.098 |
0.589 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
8 ano |
9 ano |
36 |
0.339 |
0.737 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
6 ano |
7 ano |
36 |
4.062 |
0.000 |
0.002 |
** |
| Experimental |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
6 ano |
8 ano |
36 |
3.264 |
0.002 |
0.014 |
* |
| Experimental |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
6 ano |
9 ano |
36 |
3.069 |
0.004 |
0.024 |
* |
| Experimental |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
7 ano |
8 ano |
36 |
-1.390 |
0.173 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
7 ano |
9 ano |
36 |
-1.289 |
0.205 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*Serie |
TFL.lidas.per.min.pos |
8 ano |
9 ano |
36 |
0.035 |
0.972 |
1.000 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Serie"]],
by=c("grupo","Serie","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
6 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-3.653 |
0.000 |
0.000 |
*** |
| Controle |
7 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-3.309 |
0.001 |
0.001 |
** |
| Controle |
8 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-4.949 |
0.000 |
0.000 |
**** |
| Controle |
9 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-6.030 |
0.000 |
0.000 |
**** |
| Experimental |
6 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-4.520 |
0.000 |
0.000 |
**** |
| Experimental |
7 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-1.073 |
0.287 |
0.287 |
ns |
| Experimental |
8 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-2.399 |
0.019 |
0.019 |
* |
| Experimental |
9 ano |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-2.073 |
0.042 |
0.042 |
* |
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(wdat, "TFL.lidas.per.min.pos", c("grupo","Serie"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
by=c("grupo","Serie"), suffixes = c("","'"))
}
| Controle |
6 ano |
9 |
88.444 |
9.836 |
127.778 |
7.361 |
129.020 |
4.077 |
120.752 |
137.288 |
| Controle |
7 ano |
5 |
85.000 |
12.116 |
132.800 |
16.387 |
136.577 |
5.483 |
125.457 |
147.696 |
| Controle |
8 ano |
7 |
87.143 |
9.913 |
147.571 |
7.108 |
149.771 |
4.627 |
140.388 |
159.154 |
| Controle |
9 ano |
11 |
86.000 |
6.488 |
144.727 |
5.641 |
147.768 |
3.701 |
140.261 |
155.275 |
| Experimental |
6 ano |
4 |
84.500 |
12.500 |
157.500 |
8.930 |
161.644 |
6.129 |
149.213 |
174.075 |
| Experimental |
7 ano |
2 |
103.500 |
13.500 |
128.000 |
13.000 |
118.166 |
8.714 |
100.493 |
135.840 |
| Experimental |
8 ano |
4 |
105.250 |
5.692 |
144.000 |
7.969 |
132.879 |
6.240 |
120.224 |
145.534 |
| Experimental |
9 ano |
3 |
104.333 |
6.741 |
143.000 |
4.000 |
132.553 |
7.155 |
118.042 |
147.065 |
Plots for ancova
if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "TFL.lidas.per.min.pos", c("grupo","Serie"), aov, pwcs, covar = "TFL.lidas.per.min.pre",
theme = "classic", color = color[["grupo:Serie"]],
subtitle = which(aov$Effect == "grupo:Serie"))
}
if (length(unique(pdat[["Serie"]])) >= 2) {
plots[["grupo:Serie"]] + ggplot2::ylab("Reading Words (1 Min)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "TFL.lidas.per.min", c("grupo","Serie"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Serie"]])) >= 2)
plots[["grupo:Serie"]] + ggplot2::ylab("Reading Words (1 Min)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
facet.by = c("grupo","Serie"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "grupo", facet.by = "Serie", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "Serie", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Serie"]])) >= 2)
res <- augment(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*Serie, data = wdat))
if (length(unique(pdat[["Serie"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.963 0.153
if (length(unique(pdat[["Serie"]])) >= 2)
levene_test(res, .resid ~ grupo*Serie)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 37 0.819 0.578
ANCOVA
and Pairwise for two factors
grupo:TFL.lidas.per.min.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["TFL.lidas.per.min.quintile"]]),],
"TFL.lidas.per.min.pos", c("grupo","TFL.lidas.per.min.quintile"))
pdat = pdat[pdat[["TFL.lidas.per.min.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["TFL.lidas.per.min.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["TFL.lidas.per.min.quintile"]] = factor(
pdat[["TFL.lidas.per.min.quintile"]],
level[["TFL.lidas.per.min.quintile"]][level[["TFL.lidas.per.min.quintile"]] %in% unique(pdat[["TFL.lidas.per.min.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","TFL.lidas.per.min.quintile")], pdat[,c("id","grupo","TFL.lidas.per.min.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["TFL.lidas.per.min"]] <- c(pdat[["TFL.lidas.per.min.pre"]], pdat[["TFL.lidas.per.min.pos"]])
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
aov = anova_test(pdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*TFL.lidas.per.min.quintile)
laov[["grupo:TFL.lidas.per.min.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["TFL.lidas.per.min.quintile"]] <- emmeans_test(
group_by(pdat, grupo), TFL.lidas.per.min.pos ~ TFL.lidas.per.min.quintile,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, TFL.lidas.per.min.quintile), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["TFL.lidas.per.min.quintile"]])
pwc <- pwc[,c("grupo","TFL.lidas.per.min.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","TFL.lidas.per.min.quintile")])]
}
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","TFL.lidas.per.min.quintile")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:TFL.lidas.per.min.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "TFL.lidas.per.min.pos", c("grupo","TFL.lidas.per.min.quintile"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","TFL.lidas.per.min.quintile"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","TFL.lidas.per.min.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","TFL.lidas.per.min.quintile","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","TFL.lidas.per.min.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:TFL.lidas.per.min.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*TFL.lidas.per.min.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","TFL.lidas.per.min.quintile")], wdat[,c("id","grupo","TFL.lidas.per.min.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["TFL.lidas.per.min"]] <- c(wdat[["TFL.lidas.per.min.pre"]], wdat[["TFL.lidas.per.min.pos"]])
ldat[["grupo:TFL.lidas.per.min.quintile"]] = wdat
(non.normal)
}
## [1] "P808" "P463" "P3516" "P956" "P3504" "P3721" "P1109" "P820" "P1010" "P3492" "P3581" "P1091" "P3609" "P3627" "P1094"
## [16] "P3501" "P3729" "P3566" "P3498" "P3521" "P445" "P1097" "P521" "P446" "P3502" "P812" "P1000" "P816" "P465" "P621"
## [31] "P947" "P3606" "P942" "P3495" "P618" "P628" "P950" "P241" "P451" "P515" "P448" "P3558" "P3505" "P128" "P1017"
## [46] "P1089" "P3518" "P615" "P994" "P1084" "P813" "P626" "P460" "P3512" "P3524" "P184" "P133" "P616" "P1716" "P936"
## [61] "P513" "P3615" "P464" "P512" "P3519" "P620" "P3565" "P2210" "P3560" "P176" "P523" "P3734" "P614" "P3577" "P457"
## [76] "P943" "P3509" "P613" "P3598" "P997" "P3624" "P1729" "P1004" "P516" "P1742" "P829" "P612" "P948" "P1107" "P625"
## [91] "P449" "P3578" "P941" "P953" "P958" "P3569" "P174"
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
aov = anova_test(wdat, TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*TFL.lidas.per.min.quintile)
laov[["grupo:TFL.lidas.per.min.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:TFL.lidas.per.min.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| TFL.lidas.per.min.pre |
1 |
36 |
3.613 |
0.065 |
|
0.091 |
| grupo |
1 |
36 |
0.022 |
0.882 |
|
0.001 |
| TFL.lidas.per.min.quintile |
4 |
36 |
2.148 |
0.095 |
|
0.193 |
| grupo:TFL.lidas.per.min.quintile |
4 |
36 |
0.705 |
0.594 |
|
0.073 |
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["TFL.lidas.per.min.quintile"]] <- emmeans_test(
group_by(wdat, grupo), TFL.lidas.per.min.pos ~ TFL.lidas.per.min.quintile,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, TFL.lidas.per.min.quintile), TFL.lidas.per.min.pos ~ grupo,
covariate = TFL.lidas.per.min.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["TFL.lidas.per.min.quintile"]])
pwc <- pwc[,c("grupo","TFL.lidas.per.min.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","TFL.lidas.per.min.quintile")])]
}
|
1st quintile |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
-1.106 |
0.276 |
0.276 |
ns |
|
2nd quintile |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
-0.061 |
0.951 |
0.951 |
ns |
|
3rd quintile |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
1.131 |
0.265 |
0.265 |
ns |
|
4th quintile |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
-0.433 |
0.667 |
0.667 |
ns |
|
5th quintile |
TFL.lidas.per.min.pre*grupo |
TFL.lidas.per.min.pos |
Controle |
Experimental |
36 |
-0.251 |
0.804 |
0.804 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
2nd quintile |
36 |
0.882 |
0.383 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
3rd quintile |
36 |
1.102 |
0.278 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
4th quintile |
36 |
0.725 |
0.473 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
5th quintile |
36 |
0.649 |
0.520 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
2nd quintile |
3rd quintile |
36 |
0.823 |
0.416 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
2nd quintile |
4th quintile |
36 |
0.332 |
0.742 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
2nd quintile |
5th quintile |
36 |
0.390 |
0.699 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
3rd quintile |
4th quintile |
36 |
-0.368 |
0.715 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
3rd quintile |
5th quintile |
36 |
0.019 |
0.985 |
1.000 |
ns |
| Controle |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
4th quintile |
5th quintile |
36 |
0.326 |
0.746 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
2nd quintile |
36 |
1.671 |
0.103 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
3rd quintile |
36 |
2.320 |
0.026 |
0.261 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
4th quintile |
36 |
1.286 |
0.207 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
1st quintile |
5th quintile |
36 |
1.245 |
0.221 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
2nd quintile |
3rd quintile |
36 |
1.184 |
0.244 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
2nd quintile |
4th quintile |
36 |
0.056 |
0.956 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
2nd quintile |
5th quintile |
36 |
0.260 |
0.796 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
3rd quintile |
4th quintile |
36 |
-1.703 |
0.097 |
0.972 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
3rd quintile |
5th quintile |
36 |
-0.890 |
0.379 |
1.000 |
ns |
| Experimental |
|
TFL.lidas.per.min.pre*TFL.lidas.per.min.quintile |
TFL.lidas.per.min.pos |
4th quintile |
5th quintile |
36 |
0.438 |
0.664 |
1.000 |
ns |
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","TFL.lidas.per.min.quintile")),
TFL.lidas.per.min ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:TFL.lidas.per.min.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:TFL.lidas.per.min.quintile"]],
by=c("grupo","TFL.lidas.per.min.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
1st quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-9.478 |
0 |
0 |
**** |
| Controle |
2nd quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-7.875 |
0 |
0 |
**** |
| Controle |
3rd quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-5.501 |
0 |
0 |
**** |
| Controle |
4th quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-7.562 |
0 |
0 |
**** |
| Controle |
5th quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-5.328 |
0 |
0 |
**** |
| Experimental |
1st quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-4.987 |
0 |
0 |
**** |
| Experimental |
2nd quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-4.310 |
0 |
0 |
**** |
| Experimental |
3rd quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-4.014 |
0 |
0 |
*** |
| Experimental |
4th quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-5.846 |
0 |
0 |
**** |
| Experimental |
5th quintile |
time |
TFL.lidas.per.min |
pre |
pos |
74 |
-5.125 |
0 |
0 |
**** |
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "TFL.lidas.per.min.pos", c("grupo","TFL.lidas.per.min.quintile"), covar = "TFL.lidas.per.min.pre")
ds <- merge(ds[ds$variable != "TFL.lidas.per.min.pre",],
ds[ds$variable == "TFL.lidas.per.min.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","TFL.lidas.per.min.quintile"), all.x = T, suffixes = c("", ".TFL.lidas.per.min.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","TFL.lidas.per.min.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","TFL.lidas.per.min.quintile","n","mean.TFL.lidas.per.min.pre","se.TFL.lidas.per.min.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","TFL.lidas.per.min.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:TFL.lidas.per.min.quintile"]] <- merge(ds, lemms[["grupo:TFL.lidas.per.min.quintile"]],
by=c("grupo","TFL.lidas.per.min.quintile"), suffixes = c("","'"))
}
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
| Controle |
1st quintile |
6 |
51.833 |
2.372 |
121.667 |
8.500 |
159.843 |
21.203 |
116.842 |
202.845 |
| Controle |
2nd quintile |
7 |
80.429 |
2.419 |
134.143 |
1.792 |
144.598 |
8.358 |
127.646 |
161.549 |
| Controle |
3rd quintile |
5 |
90.200 |
1.020 |
134.600 |
5.836 |
135.582 |
7.465 |
120.442 |
150.722 |
| Controle |
4th quintile |
8 |
103.000 |
1.558 |
151.250 |
5.130 |
139.823 |
8.414 |
122.758 |
156.888 |
| Controle |
5th quintile |
5 |
123.600 |
4.032 |
166.600 |
8.953 |
135.202 |
18.119 |
98.455 |
171.949 |
| Experimental |
1st quintile |
1 |
60.000 |
|
150.000 |
|
180.260 |
23.037 |
133.538 |
226.981 |
| Experimental |
2nd quintile |
2 |
68.000 |
0.000 |
123.000 |
22.000 |
145.504 |
16.697 |
111.640 |
179.368 |
| Experimental |
3rd quintile |
5 |
91.800 |
1.114 |
124.200 |
5.276 |
123.631 |
7.453 |
108.515 |
138.746 |
| Experimental |
4th quintile |
4 |
101.000 |
3.317 |
153.750 |
14.407 |
144.262 |
9.708 |
124.574 |
163.950 |
| Experimental |
5th quintile |
4 |
115.250 |
1.031 |
161.500 |
8.703 |
138.197 |
14.819 |
108.142 |
168.252 |
Plots for ancova
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "TFL.lidas.per.min.quintile", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:TFL.lidas.per.min.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["TFL.lidas.per.min.quintile"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "TFL.lidas.per.min.quintile", "grupo", aov, ylab = "Reading Words (1 Min)",
subtitle = which(aov$Effect == "grupo:TFL.lidas.per.min.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "TFL.lidas.per.min.pos", c("grupo","TFL.lidas.per.min.quintile"), aov, pwcs, covar = "TFL.lidas.per.min.pre",
theme = "classic", color = color[["grupo:TFL.lidas.per.min.quintile"]],
subtitle = which(aov$Effect == "grupo:TFL.lidas.per.min.quintile"))
}
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
plots[["grupo:TFL.lidas.per.min.quintile"]] + ggplot2::ylab("Reading Words (1 Min)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "TFL.lidas.per.min", c("grupo","TFL.lidas.per.min.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2)
plots[["grupo:TFL.lidas.per.min.quintile"]] + ggplot2::ylab("Reading Words (1 Min)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
facet.by = c("grupo","TFL.lidas.per.min.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "grupo", facet.by = "TFL.lidas.per.min.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:TFL.lidas.per.min.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2) {
ggscatter(wdat, x = "TFL.lidas.per.min.pre", y = "TFL.lidas.per.min.pos", size = 0.5,
color = "TFL.lidas.per.min.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = TFL.lidas.per.min.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:TFL.lidas.per.min.quintile"))) +
ggplot2::scale_color_manual(values = color[["TFL.lidas.per.min.quintile"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2)
res <- augment(lm(TFL.lidas.per.min.pos ~ TFL.lidas.per.min.pre + grupo*TFL.lidas.per.min.quintile, data = wdat))
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.975 0.398
if (length(unique(pdat[["TFL.lidas.per.min.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*TFL.lidas.per.min.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 9 37 1.44 0.207
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pre |
33 |
87.364 |
88.0 |
40 |
139 |
24.790 |
4.315 |
8.790 |
34.00 |
YES |
-0.119 |
-0.875 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pre |
19 |
96.947 |
96.0 |
60 |
117 |
17.520 |
4.019 |
8.444 |
23.50 |
NO |
-0.576 |
-0.738 |
|
|
|
|
|
|
TFL.lidas.per.min.pre |
52 |
90.865 |
93.0 |
40 |
139 |
22.708 |
3.149 |
6.322 |
29.50 |
YES |
-0.364 |
-0.639 |
| Controle |
|
|
|
|
|
TFL.lidas.per.min.pos |
33 |
139.000 |
136.0 |
88 |
187 |
22.809 |
3.971 |
8.088 |
20.00 |
YES |
-0.107 |
-0.220 |
| Experimental |
|
|
|
|
|
TFL.lidas.per.min.pos |
19 |
140.684 |
139.0 |
101 |
195 |
23.657 |
5.427 |
11.403 |
27.00 |
NO |
0.528 |
-0.181 |
|
|
|
|
|
|
TFL.lidas.per.min.pos |
52 |
139.615 |
139.0 |
88 |
195 |
22.905 |
3.176 |
6.377 |
20.50 |
YES |
0.148 |
-0.038 |
| Controle |
F |
|
|
|
|
TFL.lidas.per.min.pre |
32 |
93.125 |
95.0 |
40 |
154 |
23.596 |
4.171 |
8.507 |
20.25 |
YES |
0.013 |
0.538 |
| Controle |
M |
|
|
|
|
TFL.lidas.per.min.pre |
47 |
91.213 |
93.0 |
27 |
141 |
24.340 |
3.550 |
7.146 |
33.50 |
YES |
-0.243 |
-0.321 |
| Experimental |
F |
|
|
|
|
TFL.lidas.per.min.pre |
19 |
101.474 |
97.0 |
76 |
135 |
14.331 |
3.288 |
6.907 |
24.00 |
YES |
0.487 |
-0.482 |
| Experimental |
M |
|
|
|
|
TFL.lidas.per.min.pre |
30 |
89.233 |
89.0 |
54 |
135 |
19.833 |
3.621 |
7.406 |
19.00 |
YES |
0.170 |
-0.496 |
| Controle |
F |
|
|
|
|
TFL.lidas.per.min.pos |
32 |
115.625 |
117.0 |
87 |
158 |
20.863 |
3.688 |
7.522 |
39.00 |
YES |
0.311 |
-1.184 |
| Controle |
M |
|
|
|
|
TFL.lidas.per.min.pos |
47 |
101.213 |
99.0 |
43 |
150 |
23.890 |
3.485 |
7.014 |
31.00 |
YES |
-0.053 |
-0.743 |
| Experimental |
F |
|
|
|
|
TFL.lidas.per.min.pos |
19 |
119.316 |
115.0 |
82 |
151 |
20.675 |
4.743 |
9.965 |
38.50 |
YES |
0.055 |
-1.295 |
| Experimental |
M |
|
|
|
|
TFL.lidas.per.min.pos |
30 |
104.733 |
100.0 |
67 |
166 |
22.451 |
4.099 |
8.383 |
22.75 |
NO |
0.594 |
0.052 |
| Controle |
|
Rural |
|
|
|
TFL.lidas.per.min.pre |
55 |
92.764 |
95.0 |
27 |
154 |
25.675 |
3.462 |
6.941 |
34.00 |
YES |
-0.221 |
-0.119 |
| Controle |
|
Urbana |
|
|
|
TFL.lidas.per.min.pre |
7 |
93.000 |
99.0 |
48 |
124 |
24.055 |
9.092 |
22.248 |
19.00 |
NO |
-0.586 |
-0.815 |
| Experimental |
|
Rural |
|
|
|
TFL.lidas.per.min.pre |
32 |
91.656 |
92.5 |
54 |
135 |
18.074 |
3.195 |
6.517 |
17.50 |
YES |
0.074 |
-0.215 |
| Experimental |
|
Urbana |
|
|
|
TFL.lidas.per.min.pre |
5 |
101.200 |
95.0 |
90 |
135 |
19.097 |
8.540 |
23.712 |
6.00 |
NO |
1.017 |
-0.987 |
| Controle |
|
Rural |
|
|
|
TFL.lidas.per.min.pos |
55 |
103.491 |
104.0 |
43 |
150 |
22.105 |
2.981 |
5.976 |
29.50 |
YES |
-0.176 |
-0.287 |
| Controle |
|
Urbana |
|
|
|
TFL.lidas.per.min.pos |
7 |
142.857 |
150.0 |
88 |
176 |
28.399 |
10.734 |
26.264 |
23.00 |
NO |
-0.735 |
-0.690 |
| Experimental |
|
Rural |
|
|
|
TFL.lidas.per.min.pos |
32 |
104.656 |
101.5 |
70 |
141 |
17.138 |
3.030 |
6.179 |
20.00 |
YES |
0.280 |
-0.333 |
| Experimental |
|
Urbana |
|
|
|
TFL.lidas.per.min.pos |
5 |
122.200 |
115.0 |
99 |
151 |
19.677 |
8.800 |
24.433 |
16.00 |
YES |
0.298 |
-1.681 |
| Controle |
|
|
Parda |
|
|
TFL.lidas.per.min.pre |
41 |
91.707 |
95.0 |
27 |
135 |
23.364 |
3.649 |
7.374 |
33.00 |
YES |
-0.488 |
-0.157 |
| Controle |
|
|
Branca |
|
|
TFL.lidas.per.min.pre |
11 |
106.909 |
105.0 |
90 |
141 |
15.865 |
4.783 |
10.658 |
24.50 |
NO |
0.687 |
-0.662 |
| Experimental |
|
|
Parda |
|
|
TFL.lidas.per.min.pre |
16 |
88.688 |
86.0 |
54 |
135 |
20.261 |
5.065 |
10.796 |
17.00 |
NO |
0.549 |
-0.209 |
| Experimental |
|
|
Branca |
|
|
TFL.lidas.per.min.pre |
5 |
104.000 |
96.0 |
95 |
117 |
11.874 |
5.310 |
14.744 |
22.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Parda |
|
|
TFL.lidas.per.min.pos |
41 |
99.000 |
96.0 |
43 |
151 |
22.087 |
3.449 |
6.972 |
29.00 |
YES |
0.046 |
-0.103 |
| Controle |
|
|
Branca |
|
|
TFL.lidas.per.min.pos |
11 |
112.273 |
116.0 |
87 |
135 |
15.219 |
4.589 |
10.224 |
17.00 |
YES |
-0.325 |
-1.245 |
| Experimental |
|
|
Parda |
|
|
TFL.lidas.per.min.pos |
16 |
100.750 |
97.0 |
70 |
145 |
19.039 |
4.760 |
10.145 |
10.75 |
NO |
0.737 |
0.167 |
| Experimental |
|
|
Branca |
|
|
TFL.lidas.per.min.pos |
5 |
126.600 |
121.0 |
110 |
151 |
18.609 |
8.322 |
23.106 |
31.00 |
YES |
0.254 |
-2.082 |
| Controle |
|
|
|
6 ano |
|
TFL.lidas.per.min.pre |
9 |
88.444 |
93.0 |
40 |
139 |
29.509 |
9.836 |
22.683 |
11.00 |
YES |
-0.198 |
-0.835 |
| Controle |
|
|
|
7 ano |
|
TFL.lidas.per.min.pre |
5 |
85.000 |
90.0 |
54 |
120 |
27.092 |
12.116 |
33.640 |
37.00 |
YES |
0.038 |
-1.982 |
| Controle |
|
|
|
8 ano |
|
TFL.lidas.per.min.pre |
7 |
87.143 |
86.0 |
54 |
124 |
26.226 |
9.913 |
24.255 |
35.00 |
YES |
-0.059 |
-1.681 |
| Controle |
|
|
|
9 ano |
|
TFL.lidas.per.min.pre |
11 |
86.000 |
85.0 |
54 |
117 |
21.517 |
6.488 |
14.456 |
31.00 |
YES |
-0.170 |
-1.457 |
| Experimental |
|
|
|
6 ano |
|
TFL.lidas.per.min.pre |
4 |
84.500 |
82.0 |
60 |
114 |
25.000 |
12.500 |
39.781 |
34.50 |
YES |
0.128 |
-2.226 |
| Experimental |
|
|
|
7 ano |
|
TFL.lidas.per.min.pre |
2 |
103.500 |
103.5 |
90 |
117 |
19.092 |
13.500 |
171.534 |
13.50 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
8 ano |
|
TFL.lidas.per.min.pre |
4 |
105.250 |
104.5 |
95 |
117 |
11.383 |
5.692 |
18.114 |
18.25 |
YES |
0.037 |
-2.389 |
| Experimental |
|
|
|
9 ano |
|
TFL.lidas.per.min.pre |
3 |
104.333 |
102.0 |
94 |
117 |
11.676 |
6.741 |
29.005 |
11.50 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
6 ano |
|
TFL.lidas.per.min.pos |
9 |
127.778 |
135.0 |
88 |
158 |
22.084 |
7.361 |
16.975 |
21.00 |
YES |
-0.373 |
-1.158 |
| Controle |
|
|
|
7 ano |
|
TFL.lidas.per.min.pos |
5 |
132.800 |
126.0 |
99 |
187 |
36.643 |
16.387 |
45.498 |
48.00 |
YES |
0.391 |
-1.788 |
| Controle |
|
|
|
8 ano |
|
TFL.lidas.per.min.pos |
7 |
147.571 |
136.0 |
130 |
176 |
18.805 |
7.108 |
17.392 |
25.50 |
NO |
0.535 |
-1.723 |
| Controle |
|
|
|
9 ano |
|
TFL.lidas.per.min.pos |
11 |
144.727 |
139.0 |
106 |
176 |
18.709 |
5.641 |
12.569 |
18.00 |
YES |
-0.227 |
-0.413 |
| Experimental |
|
|
|
6 ano |
|
TFL.lidas.per.min.pos |
4 |
157.500 |
150.5 |
145 |
184 |
17.861 |
8.930 |
28.420 |
10.50 |
NO |
0.700 |
-1.716 |
| Experimental |
|
|
|
7 ano |
|
TFL.lidas.per.min.pos |
2 |
128.000 |
128.0 |
115 |
141 |
18.385 |
13.000 |
165.181 |
13.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
8 ano |
|
TFL.lidas.per.min.pos |
4 |
144.000 |
140.0 |
130 |
166 |
15.937 |
7.969 |
25.360 |
16.50 |
YES |
0.443 |
-1.918 |
| Experimental |
|
|
|
9 ano |
|
TFL.lidas.per.min.pos |
3 |
143.000 |
139.0 |
139 |
151 |
6.928 |
4.000 |
17.211 |
6.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pre |
6 |
51.833 |
54.0 |
40 |
55 |
5.811 |
2.372 |
6.098 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pre |
7 |
80.429 |
83.0 |
68 |
86 |
6.399 |
2.419 |
5.918 |
6.00 |
NO |
-0.913 |
-0.798 |
| Controle |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pre |
5 |
90.200 |
90.0 |
88 |
93 |
2.280 |
1.020 |
2.831 |
4.00 |
YES |
0.109 |
-2.121 |
| Controle |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pre |
8 |
103.000 |
102.0 |
99 |
110 |
4.408 |
1.558 |
3.685 |
6.25 |
NO |
0.534 |
-1.520 |
| Controle |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pre |
5 |
123.600 |
120.0 |
117 |
139 |
9.017 |
4.032 |
11.196 |
6.00 |
NO |
0.857 |
-1.206 |
| Experimental |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pre |
1 |
60.000 |
60.0 |
60 |
60 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pre |
2 |
68.000 |
68.0 |
68 |
68 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pre |
5 |
91.800 |
90.0 |
90 |
95 |
2.490 |
1.114 |
3.092 |
4.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pre |
4 |
101.000 |
99.0 |
96 |
110 |
6.633 |
3.317 |
10.555 |
8.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pre |
4 |
115.250 |
115.5 |
113 |
117 |
2.062 |
1.031 |
3.280 |
3.25 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pos |
6 |
121.667 |
120.5 |
99 |
149 |
20.820 |
8.500 |
21.849 |
30.50 |
YES |
0.104 |
-2.083 |
| Controle |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pos |
7 |
134.143 |
135.0 |
126 |
139 |
4.741 |
1.792 |
4.385 |
5.50 |
NO |
-0.503 |
-1.355 |
| Controle |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pos |
5 |
134.600 |
135.0 |
114 |
150 |
13.050 |
5.836 |
16.204 |
4.00 |
YES |
-0.450 |
-1.368 |
| Controle |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pos |
8 |
151.250 |
150.5 |
129 |
171 |
14.509 |
5.130 |
12.130 |
18.75 |
YES |
-0.157 |
-1.543 |
| Controle |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pos |
5 |
166.600 |
176.0 |
136 |
187 |
20.020 |
8.953 |
24.858 |
18.00 |
YES |
-0.477 |
-1.667 |
| Experimental |
|
|
|
|
1st quintile |
TFL.lidas.per.min.pos |
1 |
150.000 |
150.0 |
150 |
150 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
2nd quintile |
TFL.lidas.per.min.pos |
2 |
123.000 |
123.0 |
101 |
145 |
31.113 |
22.000 |
279.537 |
22.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
3rd quintile |
TFL.lidas.per.min.pos |
5 |
124.200 |
117.0 |
115 |
139 |
11.798 |
5.276 |
14.650 |
20.00 |
YES |
0.313 |
-2.189 |
| Experimental |
|
|
|
|
4th quintile |
TFL.lidas.per.min.pos |
4 |
153.750 |
145.0 |
130 |
195 |
28.814 |
14.407 |
45.850 |
25.25 |
NO |
0.560 |
-1.817 |
| Experimental |
|
|
|
|
5th quintile |
TFL.lidas.per.min.pos |
4 |
161.500 |
158.5 |
145 |
184 |
17.407 |
8.703 |
27.698 |
21.00 |
YES |
0.276 |
-2.066 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
49 |
0.929 |
0.340 |
|
0.019 |
1 |
141 |
0.001 |
0.972 |
|
0.000 |
| 2 |
TFL.lidas.per.min.pre |
1 |
49 |
39.759 |
0.000 |
* |
0.448 |
1 |
141 |
50.978 |
0.000 |
* |
0.266 |
| 4 |
grupo:Sexo |
1 |
123 |
0.986 |
0.323 |
|
0.008 |
1 |
139 |
0.076 |
0.783 |
|
0.001 |
| 5 |
Sexo |
1 |
123 |
11.520 |
0.001 |
* |
0.086 |
1 |
139 |
7.912 |
0.006 |
* |
0.054 |
| 8 |
grupo:Zona |
1 |
94 |
8.262 |
0.005 |
* |
0.081 |
1 |
101 |
1.833 |
0.179 |
|
0.018 |
| 10 |
Zona |
1 |
94 |
34.869 |
0.000 |
* |
0.271 |
1 |
101 |
18.781 |
0.000 |
* |
0.157 |
| 11 |
Cor.Raca |
1 |
68 |
2.633 |
0.109 |
|
0.037 |
1 |
70 |
2.592 |
0.112 |
|
0.036 |
| 13 |
grupo:Cor.Raca |
1 |
68 |
1.775 |
0.187 |
|
0.025 |
1 |
70 |
0.839 |
0.363 |
|
0.012 |
| 16 |
grupo:Serie |
3 |
36 |
10.000 |
0.000 |
* |
0.455 |
3 |
135 |
2.266 |
0.084 |
|
0.048 |
| 17 |
Serie |
3 |
36 |
2.179 |
0.107 |
|
0.154 |
3 |
135 |
1.687 |
0.173 |
|
0.036 |
| 20 |
grupo:TFL.lidas.per.min.quintile |
4 |
36 |
0.705 |
0.594 |
|
0.073 |
4 |
133 |
0.701 |
0.592 |
|
0.021 |
| 22 |
TFL.lidas.per.min.quintile |
4 |
36 |
2.148 |
0.095 |
|
0.193 |
4 |
133 |
0.977 |
0.422 |
|
0.029 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
|
pre |
pos |
100 |
-9.206 |
0.000 |
0.000 |
**** |
284 |
-5.744 |
0.000 |
0.000 |
**** |
| Experimental |
|
|
|
|
|
pre |
pos |
100 |
-5.917 |
0.000 |
0.000 |
**** |
284 |
-4.298 |
0.000 |
0.000 |
**** |
|
|
|
|
|
|
Controle |
Experimental |
49 |
0.964 |
0.340 |
0.340 |
ns |
141 |
-0.035 |
0.972 |
0.972 |
ns |
| Controle |
F |
|
|
|
|
pre |
pos |
248 |
-4.063 |
0.000 |
0.000 |
**** |
280 |
-4.627 |
0.000 |
0.000 |
**** |
| Controle |
M |
|
|
|
|
pre |
pos |
248 |
-2.189 |
0.030 |
0.030 |
* |
280 |
-3.774 |
0.000 |
0.000 |
*** |
| Controle |
|
|
|
|
|
F |
M |
123 |
3.299 |
0.001 |
0.001 |
** |
139 |
2.074 |
0.040 |
0.040 |
* |
| Experimental |
F |
|
|
|
|
pre |
pos |
248 |
-2.483 |
0.014 |
0.014 |
* |
280 |
-3.562 |
0.000 |
0.000 |
*** |
| Experimental |
M |
|
|
|
|
pre |
pos |
248 |
-2.710 |
0.007 |
0.007 |
** |
280 |
-2.717 |
0.007 |
0.007 |
** |
| Experimental |
|
|
|
|
|
F |
M |
123 |
1.293 |
0.198 |
0.198 |
ns |
139 |
1.934 |
0.055 |
0.055 |
ns |
|
F |
|
|
|
|
Controle |
Experimental |
123 |
0.332 |
0.741 |
0.741 |
ns |
139 |
-0.208 |
0.836 |
0.836 |
ns |
|
M |
|
|
|
|
Controle |
Experimental |
123 |
-1.177 |
0.241 |
0.241 |
ns |
139 |
0.182 |
0.856 |
0.856 |
ns |
| Controle |
|
|
|
|
|
Rural |
Urbana |
94 |
-6.379 |
0.000 |
0.000 |
**** |
101 |
-4.128 |
0.000 |
0.000 |
**** |
| Controle |
|
Rural |
|
|
|
pre |
pos |
190 |
-2.549 |
0.012 |
0.012 |
* |
204 |
-3.169 |
0.002 |
0.002 |
** |
| Controle |
|
Urbana |
|
|
|
pre |
pos |
190 |
-4.227 |
0.000 |
0.000 |
**** |
204 |
-3.822 |
0.000 |
0.000 |
*** |
| Experimental |
|
|
|
|
|
Rural |
Urbana |
94 |
-1.565 |
0.121 |
0.121 |
ns |
101 |
-1.895 |
0.061 |
0.061 |
ns |
| Experimental |
|
Rural |
|
|
|
pre |
pos |
190 |
-2.356 |
0.019 |
0.019 |
* |
204 |
-3.198 |
0.002 |
0.002 |
** |
| Experimental |
|
Urbana |
|
|
|
pre |
pos |
190 |
-1.505 |
0.134 |
0.134 |
ns |
204 |
-2.247 |
0.026 |
0.026 |
* |
|
|
Rural |
|
|
|
Controle |
Experimental |
94 |
-0.545 |
0.587 |
0.587 |
ns |
101 |
-0.806 |
0.422 |
0.422 |
ns |
|
|
Urbana |
|
|
|
Controle |
Experimental |
94 |
2.869 |
0.005 |
0.005 |
** |
101 |
1.141 |
0.256 |
0.256 |
ns |
| Controle |
|
|
Branca |
|
|
pre |
pos |
138 |
-0.605 |
0.546 |
0.546 |
ns |
142 |
-1.097 |
0.275 |
0.275 |
ns |
| Controle |
|
|
|
|
|
Parda |
Branca |
68 |
-0.658 |
0.513 |
0.513 |
ns |
70 |
-0.897 |
0.373 |
0.373 |
ns |
| Controle |
|
|
Parda |
|
|
pre |
pos |
138 |
-1.587 |
0.115 |
0.115 |
ns |
142 |
-1.803 |
0.073 |
0.073 |
ns |
| Experimental |
|
|
Branca |
|
|
pre |
pos |
138 |
-1.718 |
0.088 |
0.088 |
ns |
142 |
-1.614 |
0.109 |
0.109 |
ns |
| Experimental |
|
|
|
|
|
Parda |
Branca |
68 |
-2.019 |
0.047 |
0.047 |
* |
70 |
-1.652 |
0.103 |
0.103 |
ns |
| Experimental |
|
|
Parda |
|
|
pre |
pos |
138 |
-1.640 |
0.103 |
0.103 |
ns |
142 |
-1.541 |
0.126 |
0.126 |
ns |
|
|
|
Branca |
|
|
Controle |
Experimental |
68 |
-1.965 |
0.054 |
0.054 |
ns |
70 |
-1.271 |
0.208 |
0.208 |
ns |
|
|
|
Parda |
|
|
Controle |
Experimental |
68 |
-0.818 |
0.416 |
0.416 |
ns |
70 |
-0.407 |
0.685 |
0.685 |
ns |
| Controle |
|
|
|
6 ano |
|
pre |
pos |
74 |
-3.653 |
0.000 |
0.000 |
*** |
272 |
-2.118 |
0.035 |
0.035 |
* |
| Controle |
|
|
|
7 ano |
|
pre |
pos |
74 |
-3.309 |
0.001 |
0.001 |
** |
272 |
-2.124 |
0.035 |
0.035 |
* |
| Controle |
|
|
|
8 ano |
|
pre |
pos |
74 |
-4.949 |
0.000 |
0.000 |
**** |
272 |
-3.765 |
0.000 |
0.000 |
*** |
| Controle |
|
|
|
9 ano |
|
pre |
pos |
74 |
-6.030 |
0.000 |
0.000 |
**** |
272 |
-3.795 |
0.000 |
0.000 |
*** |
| Controle |
|
|
|
|
|
6 ano |
7 ano |
36 |
-1.107 |
0.275 |
1.000 |
ns |
135 |
-0.902 |
0.369 |
1.000 |
ns |
| Controle |
|
|
|
|
|
6 ano |
8 ano |
36 |
-3.368 |
0.002 |
0.011 |
* |
135 |
-2.815 |
0.006 |
0.034 |
* |
| Controle |
|
|
|
|
|
6 ano |
9 ano |
36 |
-3.410 |
0.002 |
0.010 |
** |
135 |
-2.311 |
0.022 |
0.134 |
ns |
| Controle |
|
|
|
|
|
7 ano |
8 ano |
36 |
-1.843 |
0.074 |
0.441 |
ns |
135 |
-1.794 |
0.075 |
0.451 |
ns |
| Controle |
|
|
|
|
|
7 ano |
9 ano |
36 |
-1.697 |
0.098 |
0.589 |
ns |
135 |
-1.166 |
0.246 |
1.000 |
ns |
| Controle |
|
|
|
|
|
8 ano |
9 ano |
36 |
0.339 |
0.737 |
1.000 |
ns |
135 |
0.830 |
0.408 |
1.000 |
ns |
| Experimental |
|
|
|
6 ano |
|
pre |
pos |
74 |
-4.520 |
0.000 |
0.000 |
**** |
272 |
-3.339 |
0.001 |
0.001 |
*** |
| Experimental |
|
|
|
7 ano |
|
pre |
pos |
74 |
-1.073 |
0.287 |
0.287 |
ns |
272 |
-1.303 |
0.194 |
0.194 |
ns |
| Experimental |
|
|
|
8 ano |
|
pre |
pos |
74 |
-2.399 |
0.019 |
0.019 |
* |
272 |
-1.964 |
0.051 |
0.051 |
ns |
| Experimental |
|
|
|
9 ano |
|
pre |
pos |
74 |
-2.073 |
0.042 |
0.042 |
* |
272 |
-1.719 |
0.087 |
0.087 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
7 ano |
36 |
4.062 |
0.000 |
0.002 |
** |
135 |
1.322 |
0.189 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
8 ano |
36 |
3.264 |
0.002 |
0.014 |
* |
135 |
0.760 |
0.448 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
9 ano |
36 |
3.069 |
0.004 |
0.024 |
* |
135 |
0.400 |
0.690 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
7 ano |
8 ano |
36 |
-1.390 |
0.173 |
1.000 |
ns |
135 |
-0.555 |
0.580 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
7 ano |
9 ano |
36 |
-1.289 |
0.205 |
1.000 |
ns |
135 |
-0.786 |
0.433 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
8 ano |
9 ano |
36 |
0.035 |
0.972 |
1.000 |
ns |
135 |
-0.277 |
0.782 |
1.000 |
ns |
|
|
|
|
6 ano |
|
Controle |
Experimental |
36 |
-4.437 |
0.000 |
0.000 |
**** |
135 |
-2.004 |
0.047 |
0.047 |
* |
|
|
|
|
7 ano |
|
Controle |
Experimental |
36 |
1.780 |
0.083 |
0.083 |
ns |
135 |
0.478 |
0.633 |
0.633 |
ns |
|
|
|
|
8 ano |
|
Controle |
Experimental |
36 |
2.164 |
0.037 |
0.037 |
* |
135 |
1.517 |
0.132 |
0.132 |
ns |
|
|
|
|
9 ano |
|
Controle |
Experimental |
36 |
1.877 |
0.069 |
0.069 |
ns |
135 |
0.507 |
0.613 |
0.613 |
ns |
| Controle |
|
|
|
|
1st quintile |
pre |
pos |
74 |
-9.478 |
0.000 |
0.000 |
**** |
268 |
-6.107 |
0.000 |
0.000 |
**** |
| Controle |
|
|
|
|
2nd quintile |
pre |
pos |
74 |
-7.875 |
0.000 |
0.000 |
**** |
268 |
-4.508 |
0.000 |
0.000 |
**** |
| Controle |
|
|
|
|
3rd quintile |
pre |
pos |
74 |
-5.501 |
0.000 |
0.000 |
**** |
268 |
-2.350 |
0.020 |
0.020 |
* |
| Controle |
|
|
|
|
4th quintile |
pre |
pos |
74 |
-7.562 |
0.000 |
0.000 |
**** |
268 |
-4.057 |
0.000 |
0.000 |
**** |
| Controle |
|
|
|
|
5th quintile |
pre |
pos |
74 |
-5.328 |
0.000 |
0.000 |
**** |
268 |
-1.260 |
0.209 |
0.209 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
2nd quintile |
36 |
0.882 |
0.383 |
1.000 |
ns |
133 |
0.295 |
0.768 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
3rd quintile |
36 |
1.102 |
0.278 |
1.000 |
ns |
133 |
0.591 |
0.555 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
4th quintile |
36 |
0.725 |
0.473 |
1.000 |
ns |
133 |
-0.123 |
0.902 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
5th quintile |
36 |
0.649 |
0.520 |
1.000 |
ns |
133 |
0.147 |
0.883 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
3rd quintile |
36 |
0.823 |
0.416 |
1.000 |
ns |
133 |
0.569 |
0.570 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
4th quintile |
36 |
0.332 |
0.742 |
1.000 |
ns |
133 |
-0.515 |
0.608 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
5th quintile |
36 |
0.390 |
0.699 |
1.000 |
ns |
133 |
0.002 |
0.998 |
1.000 |
ns |
| Controle |
|
|
|
|
|
3rd quintile |
4th quintile |
36 |
-0.368 |
0.715 |
1.000 |
ns |
133 |
-1.229 |
0.221 |
1.000 |
ns |
| Controle |
|
|
|
|
|
3rd quintile |
5th quintile |
36 |
0.019 |
0.985 |
1.000 |
ns |
133 |
-0.413 |
0.681 |
1.000 |
ns |
| Controle |
|
|
|
|
|
4th quintile |
5th quintile |
36 |
0.326 |
0.746 |
1.000 |
ns |
133 |
0.556 |
0.579 |
1.000 |
ns |
| Experimental |
|
|
|
|
1st quintile |
pre |
pos |
74 |
-4.987 |
0.000 |
0.000 |
**** |
268 |
-3.280 |
0.001 |
0.001 |
** |
| Experimental |
|
|
|
|
2nd quintile |
pre |
pos |
74 |
-4.310 |
0.000 |
0.000 |
**** |
268 |
-2.185 |
0.030 |
0.030 |
* |
| Experimental |
|
|
|
|
3rd quintile |
pre |
pos |
74 |
-4.014 |
0.000 |
0.000 |
*** |
268 |
-2.730 |
0.007 |
0.007 |
** |
| Experimental |
|
|
|
|
4th quintile |
pre |
pos |
74 |
-5.846 |
0.000 |
0.000 |
**** |
268 |
-3.146 |
0.002 |
0.002 |
** |
| Experimental |
|
|
|
|
5th quintile |
pre |
pos |
74 |
-5.125 |
0.000 |
0.000 |
**** |
268 |
-2.593 |
0.010 |
0.010 |
* |
| Experimental |
|
|
|
|
|
1st quintile |
2nd quintile |
36 |
1.671 |
0.103 |
1.000 |
ns |
133 |
0.949 |
0.344 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
3rd quintile |
36 |
2.320 |
0.026 |
0.261 |
ns |
133 |
0.252 |
0.802 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
4th quintile |
36 |
1.286 |
0.207 |
1.000 |
ns |
133 |
-0.247 |
0.805 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
5th quintile |
36 |
1.245 |
0.221 |
1.000 |
ns |
133 |
-0.319 |
0.750 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
3rd quintile |
36 |
1.184 |
0.244 |
1.000 |
ns |
133 |
-0.873 |
0.384 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
4th quintile |
36 |
0.056 |
0.956 |
1.000 |
ns |
133 |
-1.450 |
0.149 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
5th quintile |
36 |
0.260 |
0.796 |
1.000 |
ns |
133 |
-1.280 |
0.203 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
3rd quintile |
4th quintile |
36 |
-1.703 |
0.097 |
0.972 |
ns |
133 |
-0.835 |
0.405 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
3rd quintile |
5th quintile |
36 |
-0.890 |
0.379 |
1.000 |
ns |
133 |
-0.882 |
0.380 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
4th quintile |
5th quintile |
36 |
0.438 |
0.664 |
1.000 |
ns |
133 |
-0.228 |
0.820 |
1.000 |
ns |
|
|
|
|
|
1st quintile |
Controle |
Experimental |
36 |
-1.106 |
0.276 |
0.276 |
ns |
133 |
0.044 |
0.965 |
0.965 |
ns |
|
|
|
|
|
2nd quintile |
Controle |
Experimental |
36 |
-0.061 |
0.951 |
0.951 |
ns |
133 |
1.187 |
0.237 |
0.237 |
ns |
|
|
|
|
|
3rd quintile |
Controle |
Experimental |
36 |
1.131 |
0.265 |
0.265 |
ns |
133 |
-0.432 |
0.666 |
0.666 |
ns |
|
|
|
|
|
4th quintile |
Controle |
Experimental |
36 |
-0.433 |
0.667 |
0.667 |
ns |
133 |
-0.209 |
0.835 |
0.835 |
ns |
|
|
|
|
|
5th quintile |
Controle |
Experimental |
36 |
-0.251 |
0.804 |
0.804 |
ns |
133 |
-1.101 |
0.273 |
0.273 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
|
33 |
87.364 |
4.315 |
139.000 |
3.971 |
141.414 |
3.045 |
135.295 |
147.532 |
91 |
91.923 |
2.528 |
113.593 |
2.988 |
113.974 |
2.535 |
108.962 |
118.987 |
-58 |
| Experimental |
|
|
|
|
|
19 |
96.947 |
4.019 |
140.684 |
5.427 |
136.492 |
4.036 |
128.382 |
144.602 |
53 |
93.528 |
2.648 |
114.774 |
3.767 |
114.119 |
3.323 |
107.550 |
120.688 |
-34 |
| Controle |
F |
|
|
|
|
32 |
93.125 |
4.171 |
115.625 |
3.688 |
115.384 |
3.080 |
109.287 |
121.480 |
36 |
93.917 |
4.018 |
121.194 |
4.358 |
120.332 |
3.949 |
112.523 |
128.140 |
-4 |
| Controle |
M |
|
|
|
|
47 |
91.213 |
3.550 |
101.213 |
3.485 |
102.203 |
2.544 |
97.168 |
107.238 |
55 |
90.618 |
3.270 |
108.618 |
3.926 |
109.784 |
3.198 |
103.461 |
116.107 |
-8 |
| Experimental |
F |
|
|
|
|
19 |
101.474 |
3.288 |
119.316 |
4.743 |
113.698 |
4.045 |
105.691 |
121.705 |
22 |
98.455 |
3.725 |
125.318 |
5.550 |
121.665 |
5.077 |
111.627 |
131.703 |
-3 |
| Experimental |
M |
|
|
|
|
30 |
89.233 |
3.621 |
104.733 |
4.099 |
106.998 |
3.191 |
100.682 |
113.314 |
31 |
90.032 |
3.592 |
107.290 |
4.718 |
108.816 |
4.259 |
100.395 |
117.238 |
-1 |
| Controle |
|
Rural |
|
|
|
55 |
92.764 |
3.462 |
103.491 |
2.981 |
103.544 |
2.066 |
99.442 |
107.646 |
58 |
91.914 |
3.445 |
106.276 |
3.302 |
106.433 |
2.860 |
100.760 |
112.107 |
-3 |
| Controle |
|
Urbana |
|
|
|
7 |
93.000 |
9.092 |
142.857 |
10.734 |
142.762 |
5.791 |
131.264 |
154.260 |
7 |
93.000 |
9.092 |
142.857 |
10.734 |
142.414 |
8.233 |
126.083 |
158.745 |
0 |
| Experimental |
|
Rural |
|
|
|
32 |
91.656 |
3.195 |
104.656 |
3.030 |
105.402 |
2.710 |
100.022 |
110.782 |
35 |
90.714 |
3.196 |
109.371 |
3.922 |
110.192 |
3.684 |
102.884 |
117.500 |
-3 |
| Experimental |
|
Urbana |
|
|
|
5 |
101.200 |
8.540 |
122.200 |
8.800 |
116.977 |
6.875 |
103.325 |
130.628 |
6 |
102.667 |
7.126 |
134.333 |
14.101 |
128.545 |
8.943 |
110.804 |
146.287 |
-1 |
| Controle |
|
|
Branca |
|
|
11 |
106.909 |
4.783 |
112.273 |
4.589 |
104.105 |
4.735 |
94.656 |
113.554 |
12 |
105.500 |
4.588 |
115.417 |
5.237 |
107.934 |
5.321 |
97.322 |
118.545 |
-1 |
| Controle |
|
|
Parda |
|
|
41 |
91.707 |
3.649 |
99.000 |
3.449 |
100.585 |
2.395 |
95.806 |
105.364 |
42 |
92.381 |
3.624 |
101.095 |
3.965 |
102.510 |
2.789 |
96.947 |
108.073 |
-1 |
| Experimental |
|
|
Branca |
|
|
5 |
104.000 |
5.310 |
126.600 |
8.322 |
120.298 |
6.883 |
106.563 |
134.033 |
5 |
104.000 |
5.310 |
126.600 |
8.322 |
120.134 |
8.118 |
103.943 |
136.325 |
0 |
| Experimental |
|
|
Parda |
|
|
16 |
88.688 |
5.065 |
100.750 |
4.760 |
104.273 |
3.848 |
96.595 |
111.951 |
16 |
88.688 |
5.065 |
100.750 |
4.760 |
104.670 |
4.544 |
95.607 |
113.732 |
0 |
| Controle |
|
|
|
6 ano |
|
9 |
88.444 |
9.836 |
127.778 |
7.361 |
129.020 |
4.077 |
120.752 |
137.288 |
31 |
89.355 |
4.599 |
103.000 |
4.601 |
105.043 |
4.265 |
96.608 |
113.478 |
-22 |
| Controle |
|
|
|
7 ano |
|
5 |
85.000 |
12.116 |
132.800 |
16.387 |
136.577 |
5.483 |
125.457 |
147.696 |
19 |
96.211 |
5.459 |
113.684 |
6.130 |
111.293 |
5.446 |
100.523 |
122.064 |
-14 |
| Controle |
|
|
|
8 ano |
|
7 |
87.143 |
9.913 |
147.571 |
7.108 |
149.771 |
4.627 |
140.388 |
159.154 |
15 |
88.667 |
6.589 |
123.533 |
8.285 |
126.022 |
6.127 |
113.903 |
138.140 |
-8 |
| Controle |
|
|
|
9 ano |
|
11 |
86.000 |
6.488 |
144.727 |
5.641 |
147.768 |
3.701 |
140.261 |
155.275 |
26 |
93.731 |
4.396 |
120.423 |
5.550 |
119.636 |
4.648 |
110.444 |
128.828 |
-15 |
| Experimental |
|
|
|
6 ano |
|
4 |
84.500 |
12.500 |
157.500 |
8.930 |
161.644 |
6.129 |
149.213 |
174.075 |
19 |
89.368 |
4.089 |
116.842 |
7.951 |
118.877 |
5.443 |
108.112 |
129.641 |
-15 |
| Experimental |
|
|
|
7 ano |
|
2 |
103.500 |
13.500 |
128.000 |
13.000 |
118.166 |
8.714 |
100.493 |
135.840 |
11 |
93.636 |
5.301 |
107.727 |
5.643 |
107.001 |
7.145 |
92.871 |
121.131 |
-9 |
| Experimental |
|
|
|
8 ano |
|
4 |
105.250 |
5.692 |
144.000 |
7.969 |
132.879 |
6.240 |
120.224 |
145.534 |
13 |
93.462 |
6.557 |
113.000 |
7.929 |
112.387 |
6.572 |
99.389 |
125.385 |
-9 |
| Experimental |
|
|
|
9 ano |
|
3 |
104.333 |
6.741 |
143.000 |
4.000 |
132.553 |
7.155 |
118.042 |
147.065 |
10 |
101.400 |
5.636 |
120.900 |
5.906 |
115.153 |
7.535 |
100.251 |
130.054 |
-7 |
| Controle |
|
|
|
|
1st quintile |
6 |
51.833 |
2.372 |
121.667 |
8.500 |
159.843 |
21.203 |
116.842 |
202.845 |
15 |
53.267 |
2.497 |
93.467 |
7.672 |
116.229 |
13.427 |
89.671 |
142.787 |
-9 |
| Controle |
|
|
|
|
2nd quintile |
7 |
80.429 |
2.419 |
134.143 |
1.792 |
144.598 |
8.358 |
127.646 |
161.549 |
19 |
78.263 |
1.563 |
104.632 |
5.663 |
112.897 |
7.046 |
98.961 |
126.832 |
-12 |
| Controle |
|
|
|
|
3rd quintile |
5 |
90.200 |
1.020 |
134.600 |
5.836 |
135.582 |
7.465 |
120.442 |
150.722 |
15 |
91.600 |
0.653 |
107.067 |
5.894 |
107.597 |
6.278 |
95.178 |
120.015 |
-10 |
| Controle |
|
|
|
|
4th quintile |
8 |
103.000 |
1.558 |
151.250 |
5.130 |
139.823 |
8.414 |
122.758 |
156.888 |
23 |
102.609 |
0.889 |
124.174 |
4.943 |
118.319 |
5.914 |
106.621 |
130.018 |
-15 |
| Controle |
|
|
|
|
5th quintile |
5 |
123.600 |
4.032 |
166.600 |
8.953 |
135.202 |
18.119 |
98.455 |
171.949 |
19 |
123.421 |
2.573 |
130.789 |
6.335 |
112.864 |
10.884 |
91.336 |
134.393 |
-14 |
| Experimental |
|
|
|
|
1st quintile |
1 |
60.000 |
|
150.000 |
|
180.260 |
23.037 |
133.538 |
226.981 |
5 |
58.600 |
2.227 |
96.000 |
14.505 |
115.669 |
14.942 |
86.114 |
145.223 |
-4 |
| Experimental |
|
|
|
|
2nd quintile |
2 |
68.000 |
0.000 |
123.000 |
22.000 |
145.504 |
16.697 |
111.640 |
179.368 |
12 |
77.417 |
1.836 |
93.500 |
5.368 |
102.256 |
8.368 |
85.703 |
118.808 |
-10 |
| Experimental |
|
|
|
|
3rd quintile |
5 |
91.800 |
1.114 |
124.200 |
5.276 |
123.631 |
7.453 |
108.515 |
138.746 |
13 |
91.923 |
0.804 |
111.231 |
3.901 |
111.573 |
6.740 |
98.242 |
124.904 |
-8 |
| Experimental |
|
|
|
|
4th quintile |
4 |
101.000 |
3.317 |
153.750 |
14.407 |
144.262 |
9.708 |
124.574 |
163.950 |
11 |
100.818 |
1.623 |
125.000 |
8.567 |
120.184 |
7.743 |
104.868 |
135.499 |
-7 |
| Experimental |
|
|
|
|
5th quintile |
4 |
115.250 |
1.031 |
161.500 |
8.703 |
138.197 |
14.819 |
108.142 |
168.252 |
12 |
119.250 |
2.161 |
138.333 |
6.446 |
122.827 |
10.704 |
101.655 |
144.000 |
-8 |